<p>This note considers nonparametric identification of a general nonlinear regression model with a dichotomous regressor subject to misclassification error. The available sample information consists of a dependent variable and a set of regressors, one of which is binary and error-ridden with misclassification error that has unknown distribution. Our identification strategy does not parameterize any regression or distribution functions, and does not require additional sample information such as instrumental variables, repeated measurements, or an auxiliary sample. Our main identifying assumption is that the regression model error has zero conditional third moment. The results include a closed-form solution for the unknown distributions and the regression function.</p>
Authors
![Arthur Lewbel](/sites/default/files/styles/square_desktop/public/2022-07/Arthur%20Lewbel.png?itok=MXnJtTf7)
Research Associate Boston College
Arthur is a Research Associate of the IFS and holds the Barbara A. and Patrick E. Roche chair in economics at Boston College.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Yale University
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Johns Hopkins University
Working Paper details
- DOI
- 10.1920/wp.cem.2007.1707
- Publisher
- IFS
Suggested citation
X, Chen and Y, Hu and A, Lewbel. (2007). Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments. London: IFS. Available at: https://ifs.org.uk/publications/nonparametric-identification-regression-models-containing-misclassified-dichotomous (accessed: 1 July 2024).
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